{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c80d61c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-10T10:03:25.874662Z",
     "start_time": "2024-04-10T10:03:23.545066Z"
    }
   },
   "source": [
    "## ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be9b7552",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T13:52:56.986117Z",
     "start_time": "2024-04-11T13:52:52.535117Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length  sepal width  petal length  petal width\n",
      "0             5.1          3.5           1.4          0.2\n",
      "1             4.9          3.0           1.4          0.2\n",
      "2             4.7          3.2           1.3          0.2\n",
      "3             4.6          3.1           1.5          0.2\n",
      "4             5.0          3.6           1.4          0.2\n",
      "..            ...          ...           ...          ...\n",
      "145           6.7          3.0           5.2          2.3\n",
      "146           6.3          2.5           5.0          1.9\n",
      "147           6.5          3.0           5.2          2.0\n",
      "148           6.2          3.4           5.4          2.3\n",
      "149           5.9          3.0           5.1          1.8\n",
      "\n",
      "[150 rows x 4 columns]               class\n",
      "0       Iris-setosa\n",
      "1       Iris-setosa\n",
      "2       Iris-setosa\n",
      "3       Iris-setosa\n",
      "4       Iris-setosa\n",
      "..              ...\n",
      "145  Iris-virginica\n",
      "146  Iris-virginica\n",
      "147  Iris-virginica\n",
      "148  Iris-virginica\n",
      "149  Iris-virginica\n",
      "\n",
      "[150 rows x 1 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, learning_curve, validation_curve, cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "\n",
    "# 从UCI机器学习库中获取数据集\n",
    "iris = fetch_ucirepo(id=53)\n",
    "X = iris.data.features\n",
    "y = iris.data.targets\n",
    "print(X,y)\n",
    "\n",
    "y = label_binarize(y, classes=np.unique(y))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "svm_clf = OneVsRestClassifier(SVC(probability=True))\n",
    "svm_clf.fit(X_train, y_train)\n",
    "\n",
    "# 计算ROC曲线\n",
    "y_score = svm_clf.predict_proba(X_test)\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(y.shape[1]): \n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plt.figure()\n",
    "colors = ['blue', 'red', 'green'] \n",
    "for i, color in zip(range(y.shape[1]), colors):\n",
    "    plt.plot(fpr[i], tpr[i], color=color, lw=2, label='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i]))\n",
    "plt.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea43d103",
   "metadata": {},
   "source": [
    "## 学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b029603e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T12:26:02.247991Z",
     "start_time": "2024-04-11T12:26:01.538042Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86189\\anaconda3\\lib\\site-packages\\sklearn\\multiclass.py:77: UserWarning: Label not 0 is present in all training examples.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86189\\anaconda3\\lib\\site-packages\\sklearn\\multiclass.py:77: UserWarning: Label not 0 is present in all training examples.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86189\\anaconda3\\lib\\site-packages\\sklearn\\multiclass.py:77: UserWarning: Label not 0 is present in all training examples.\n",
      "  warnings.warn(\n",
      "C:\\Users\\86189\\anaconda3\\lib\\site-packages\\sklearn\\multiclass.py:77: UserWarning: Label not 0 is present in all training examples.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制学习曲线\n",
    "train_sizes, train_scores, test_scores = learning_curve(svm_clf, X_train, y_train, train_sizes=np.linspace(0.1, 1.0, 10), cv=5)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_sizes, np.mean(train_scores, axis=1), 'o-', color=\"r\", label=\"Training score\")\n",
    "plt.plot(train_sizes, np.mean(test_scores, axis=1), 'o-', color=\"g\", label=\"Cross-validation score\")\n",
    "plt.xlabel(\"Training examples\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.title(\"Learning Curve\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d114ecf",
   "metadata": {},
   "source": [
    "## 验证曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d0d81a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-12T07:04:01.268992Z",
     "start_time": "2024-04-12T07:03:58.630045Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import validation_curve\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "\n",
    "iris = fetch_ucirepo(id=53)\n",
    "X = iris.data.features\n",
    "y = iris.data.targets\n",
    "\n",
    "# 创建 SVM 模型\n",
    "model = SVC(kernel='rbf', gamma='auto')\n",
    "\n",
    "# 设置参数范围\n",
    "param_range = [0.001, 0.01, 0.1, 1,10]\n",
    "\n",
    "# 绘制验证曲线\n",
    "train_scores, test_scores = validation_curve(\n",
    "    model, X, y, param_name='C', param_range=param_range,\n",
    "    cv=5, scoring='accuracy', n_jobs=-1\n",
    ")\n",
    "\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Validation Curve')\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Score')\n",
    "plt.ylim(0.9, 1.0)\n",
    "lw = 2\n",
    "\n",
    "plt.semilogx(param_range, train_scores_mean, label='Training score', color='darkorange', lw=lw)\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.2, color='darkorange')\n",
    "\n",
    "plt.semilogx(param_range, test_scores_mean, label='Cross-validation score', color='navy', lw=lw)\n",
    "plt.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.2, color='navy')\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e09c45",
   "metadata": {},
   "source": [
    "## K折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fd00df1f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T13:56:06.728950Z",
     "start_time": "2024-04-11T13:56:06.656405Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Scores: [1.         1.         0.56666667 0.86666667 0.63333333]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import SVC\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "\n",
    "iris = fetch_ucirepo(id=53)\n",
    "X = iris.data.features\n",
    "y = iris.data.targets\n",
    "\n",
    "model = SVC(kernel='rbf', gamma='auto')\n",
    "\n",
    "k = 5  \n",
    "scores = cross_val_score(model, X, y, cv=k, scoring='accuracy')\n",
    "\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6268e288",
   "metadata": {},
   "source": [
    "## 优化参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da8bb7e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-12T06:59:57.198494Z",
     "start_time": "2024-04-12T06:59:27.028674Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86189\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:909: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  self.best_estimator_.fit(X, y, **fit_params)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found:  {'max_depth': 13, 'max_features': 'sqrt', 'min_samples_leaf': 3, 'min_samples_split': 15, 'n_estimators': 729}\n",
      "Best accuracy found:  0.9523809523809523\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV,train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from scipy.stats import randint\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "\n",
    "iris = fetch_ucirepo(id=53)\n",
    "X = iris.data.features\n",
    "y = iris.data.targets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "param_dist = {\n",
    "    \"n_estimators\": randint(10, 1000),\n",
    "    \"max_depth\": randint(1, 20),\n",
    "    \"min_samples_split\": randint(2, 20),\n",
    "    \"min_samples_leaf\": randint(1, 20),\n",
    "    \"max_features\": ['auto', 'sqrt', 'log2']\n",
    "}\n",
    "\n",
    "random_search = RandomizedSearchCV(RandomForestClassifier(), param_distributions=param_dist, n_iter=100, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best parameters found: \", random_search.best_params_)\n",
    "print(\"Best accuracy found: \", random_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da6a564a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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